FMRI has become a tool for mapping brain function because of its high spatial and temporal resolution and noninvasiveness. In addition, it is applicable to single subjects and thus could play a role in the clinical realm. These properties are appealing for use in clinical populations, for research, treatment and disease monitoring. However, in contrast to normal volunteers, clinical populations are more likely to move, more likely to fail to conform to the task and are less tolerant to long imaging sessions. Thus, these subjects may generate less data and of lower quality than normal making it more difficult to detect the low signal changes inherent in BOLD contrast fMRI. In addition, qualified patients are more difficult to come by than normal making it critical to insure that useful data has been acquired. Timely feedback from results is also necessary for treatment planning. Thus, we have set out to develop a clinical competent fMRI system that is more flexible and more efficient that existing systems. The goal of the system is to provide time efficient estimation of activation in the presence of multiple concurrent effects, track processes that lead to confounding effects (motion, speech, cardiac pulsatility, respiration, etc) and adapt the processing to the subject's performance rather than the expected task. Major components of this system such as the timeaware architecture, measurement and filtering of physiologic data (speech, cardiac, respiration, skin conductance) and real-time multiple linear regression have been completed. We will extend the system by incorporatingdetection of and correction for motion, modeling of physiologic noise and development of real-time speech detection for use in a paradigm where both the timing of paradigm delivery and processing of the data is driven by the subject's response.